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SUMMARY We present a new compilation and analysis of broad-band ocean bottom seismometer noise properties from 15 yr of seismic deployments. We compile a comprehensive data set of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase and admittance) for 551 unique stations spanning 18 U.S.-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise data set serves as a building block for more targeted future investigations by the marine seismology community. 

SUMMARY Measurements of various physical properties of oceanic sediment and crustal structures provide insight into a number of geological and geophysical processes. In particular, knowledge of the shear wave velocity (VS) structure of marine sediments and oceanic crust has wide ranging implications from geotechnical engineering projects to seismic mantle tomography studies. In this study, we propose a novel approach to nonlinearly invert compliance signals recorded by colocated ocean-bottom seismometers and high-sample-rate pressure gauges for shallow oceanic shear wave velocity structure. The inversion method is based on a type of machine learning neural network known as a mixture density neural network (MDN). We demonstrate the effectiveness of the MDN method on synthetic models with a fixed deployment depth of 2015 m and show that among 30 000 test models, the inverted shear wave velocity profiles achieve an average error of 0.025 km s−1. We then apply the method to observed data recorded by a broad-band ocean-bottom station in the Lau basin, for which a VS profile was estimated using Monte Carlo sampling methods. Using the mixture density network approach, we validate the method by showing that our VS profile is in excellent agreement with the previous result. Finally, we argue that the mixture density network approach to compliance inversion is advantageous over other compliance inversion methods because it is faster and allows for standardized measurements.